b0fbd4d11d
* Add example YAML file for training Mistral using DPO * added deduplication code * Add exact deduplication feature and update examples * Improve deduplication for train/eval overlap Changed the deduplication function to use a more memory-efficient hashing method. Applied Git suggestions to improve clarity and maintainability.\n\nThe deduplication now handles cases where train and eval datasets have overlapping elements. * Improve deduplication for train/eval overlap Changed the deduplication function to use a more memory-efficient hashing method. Applied Git suggestions to improve clarity and maintainability.\n\nThe deduplication now handles cases where train and eval datasets have overlapping elements. * Apply suggestions from code review To handle the original case where we do not do deduplication Co-authored-by: Wing Lian <wing.lian@gmail.com> * Improve false collision detection to ensure dataset integrity - Added test cases to simulate and verify handling of forced hash collisions between datasets. - Ensured that datasets with identical hashes but different content are correctly identified, preventing incorrect deduplication. - Updated unit tests to include scenarios where collisions occur across both training and evaluation datasets, as well as within a single dataset. * Moved the constants file to the tests folder - Relocated `constants.py` to the `tests` folder to improve modularity and maintain a clear separation between source and test files. - Renamed `cicd/tests.py` to `cicd/cicd_tests.py` to resolve a conflict with `tests/__init__.py`, which caused Mypy to fail due to duplicate module names. - Updated all references to `cicd.tests` in the codebase to `cicd.cicd_tests` to reflect the renaming and ensure compatibility. - These changes ensure Mypy passes the pre-commit hook and maintain alignment with the project's structure. * revert some changes from previous commit and fix relative import --------- Co-authored-by: Wing Lian <wing.lian@gmail.com> Co-authored-by: Wing Lian <wing@axolotl.ai>
Llama-3
https://llama.meta.com/llama3/
- Full Fine Tune
- Single GPU @ 48GB VRAM
- LoRA
- Single GPU @ 11GB VRAM
- QLORA+FSDP
- Dual GPU @ 21GB VRAM